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ServiceNow MCP Server

activate_workflow

Initiate and execute workflows in ServiceNow by specifying the workflow ID, enabling automation of processes and task management through the MCP server integration.

Instructions

Activate a workflow in ServiceNow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Implementation Reference

  • The handler function that executes the tool: validates params, makes PATCH request to /api/now/table/wf_workflow/{id} to set active=true, returns workflow details or error.
    def activate_workflow(
        auth_manager: AuthManager,
        server_config: ServerConfig,
        params: Dict[str, Any],
    ) -> Dict[str, Any]:
        """
        Activate a workflow in ServiceNow.
        
        Args:
            auth_manager: Authentication manager
            server_config: Server configuration
            params: Parameters for activating a workflow
            
        Returns:
            Dict[str, Any]: Activated workflow details
        """
        # Unwrap parameters if needed
        params = _unwrap_params(params, ActivateWorkflowParams)
        
        # Get the correct auth_manager and server_config
        try:
            auth_manager, server_config = _get_auth_and_config(auth_manager, server_config)
        except ValueError as e:
            logger.error(f"Error getting auth and config: {e}")
            return {"error": str(e)}
        
        workflow_id = params.get("workflow_id")
        if not workflow_id:
            return {"error": "Workflow ID is required"}
        
        # Prepare data for the API request
        data = {
            "active": "true",
        }
        
        # Make the API request
        try:
            headers = auth_manager.get_headers()
            url = f"{server_config.instance_url}/api/now/table/wf_workflow/{workflow_id}"
            
            response = requests.patch(url, headers=headers, json=data)
            response.raise_for_status()
            
            result = response.json()
            return {
                "workflow": result.get("result", {}),
                "message": "Workflow activated successfully",
            }
        except requests.RequestException as e:
            logger.error(f"Error activating workflow: {e}")
            return {"error": str(e)}
        except Exception as e:
            logger.error(f"Unexpected error activating workflow: {e}")
            return {"error": str(e)}
  • Pydantic input schema defining the required 'workflow_id' parameter.
    class ActivateWorkflowParams(BaseModel):
        """Parameters for activating a workflow."""
        
        workflow_id: str = Field(..., description="Workflow ID or sys_id")
  • Registration of the tool in the central tool_definitions dictionary used by the MCP server: maps name to (handler, schema, return_type, description, serialization).
    "activate_workflow": (
        activate_workflow_tool,
        ActivateWorkflowParams,
        str,
        "Activate a workflow in ServiceNow",
        "str",  # Tool returns simple message
    ),
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden but only states the action without behavioral details. It doesn't disclose if this is a mutation (likely), requires permissions, has side effects (e.g., triggers workflows), rate limits, or error conditions. This leaves critical behavioral traits unspecified for a tool that presumably changes system state.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise—a single sentence with no wasted words—and front-loads the core action. While under-specified, it's structurally efficient, earning full marks for brevity and clarity within its limited scope.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool likely performs a mutation (activating workflows) with no annotations, no output schema, and 0% schema coverage, the description is incomplete. It lacks details on behavior, parameters, outcomes, or error handling, making it inadequate for safe and effective use by an AI agent in this context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate but adds no parameter information. It doesn't explain 'workflow_id' (e.g., format, where to find it, or if it's a sys_id), leaving the single parameter's meaning and usage unclear beyond the schema's basic title and type.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the action ('activate') and resource ('workflow in ServiceNow'), which provides a basic understanding of purpose. However, it lacks specificity about what 'activate' means operationally (e.g., enabling execution, setting status) and doesn't distinguish from sibling tools like 'deactivate_workflow' or 'create_workflow', leaving ambiguity about its unique function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., workflow must exist), exclusions, or comparisons to siblings like 'deactivate_workflow' or 'update_workflow', leaving the agent to infer usage context without explicit direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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